Environmental Sensor Data Visualization for Aquafarm Monitoring

2007 ◽  
Author(s):  
Eung Kon Kim ◽  
Heetack Ceong ◽  
Sung-Ju Jung
Keyword(s):  
Data Visualization ◽  
Sensor Data ◽  
Environmental Sensor

Enabling RDF Stream Processing for Sensor Data Management in the Environmental Domain

2016 ◽  
Vol 12 (4) ◽  
pp. 1-21 ◽  
Author(s):  
Alejandro Llaves ◽  
Oscar Corcho ◽  
Peter Taylor ◽  
Kerry Taylor
Keyword(s):  
Semantic Annotation ◽  
Distributed Processing ◽  
Stream Processing ◽  
Sensor Data ◽  
Message Processing ◽  
Observation Data ◽  
Data Formats ◽  
Average Latency ◽  
Environmental Sensor ◽  
Rdf Graphs

This paper presents a generic approach to integrate environmental sensor data efficiently, allowing the detection of relevant situations and events in near real-time through continuous querying. Data variety is addressed with the use of the Semantic Sensor Network ontology for observation data modelling, and semantic annotations for environmental phenomena. Data velocity is handled by distributing sensor data messaging and serving observations as RDF graphs on query demand. The stream processing engine presented in the paper, morph-streams++, provides adapters for different data formats and distributed processing of streams in a cluster. An evaluation of different configurations for parallelization and semantic annotation parameters proves that the described approach reduces the average latency of message processing in some cases.

Machine Learning Classification of Head Impact Sensor Data

2019 ◽  
Author(s):  
Tyler F. Rooks ◽  
Andrea S. Dargie ◽  
Valeta Carol Chancey
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
External Validation ◽  
Classification Algorithm ◽  
Sensor Data ◽  
Environmental Sensors ◽  
Head Acceleration ◽  
Machine Learning Classification ◽  
Environmental Sensor ◽  
Validation Set

Abstract A shortcoming of using environmental sensors for the surveillance of potentially concussive events is substantial uncertainty regarding whether the event was caused by head acceleration (“head impacts”) or sensor motion (with no head acceleration). The goal of the present study is to develop a machine learning model to classify environmental sensor data obtained in the field and evaluate the performance of the model against the performance of the proprietary classification algorithm used by the environmental sensor. Data were collected from Soldiers attending sparring sessions conducted under a U.S. Army Combatives School course. Data from one sparring session were used to train a decision tree classification algorithm to identify good and bad signals. Data from the remaining sparring sessions were kept as an external validation set. The performance of the proprietary algorithm used by the sensor was also compared to the trained algorithm performance. The trained decision tree was able to correctly classify 95% of events for internal cross-validation and 88% of events for the external validation set. Comparatively, the proprietary algorithm was only able to correctly classify 61% of the events. In general, the trained algorithm was better able to predict when a signal was good or bad compared to the proprietary algorithm. The present study shows it is possible to train a decision tree algorithm using environmental sensor data collected in the field.

Sensing Technologies for Societal Well-Being

2011 ◽  
Vol 3 (2) ◽  
pp. 76-84
Author(s):  
Elizabeth Avery Gomez
Keyword(s):  
Well Being ◽  
Sensor Data ◽  
Measurement Scale ◽  
Sensor Calibration ◽  
Ongoing Research ◽  
Technological Advances ◽  
Accuracy And Precision ◽  
Flexible Architecture ◽  
Application Programming ◽  
Environmental Sensor

Sensing technologies by design are calibrated for accuracy against an expected measurement scale. Sensor calibration and signal processing criteria are one type of sensor data, while the sensor readings are another. Ensuring data accuracy and precision from sensors is an essential, ongoing challenge, but these issues haven’t stopped the potential for pervasive application use. Technological advances afford an opportunity for sensor data integration as a vehicle for societal well-being and the focus of ongoing research. A lean and flexible architecture is needed to acquire sensor data for societal well-being. As such, this research places emphasis on the acquisition of environmental sensor data through lean application programming protocols (APIs) through services such as SMS, where scant literature is presented. The contribution of this research is to advance the research that integrates sensor data with pervasive applications.

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